Opening and closing can be sequentially used to remove noise (small foreground objects) from a binary image. This can be used in cleaning a fingerprint image as a preprocessing step. The following code block demonstrates how to implement it:

im = rgb2gray(imread('../images/fingerprint.jpg'))
im[im <= 0.5] = 0 # binarize
im[im > 0.5] = 1
im_o = binary_opening(im, square(2))
im_c = binary_closing(im, square(2))
im_oc = binary_closing(binary_opening(im, square(2)), square(2))
pylab.figure(figsize=(20,20))
pylab.subplot(221), plot_image(im, 'original')
pylab.subplot(222), plot_image(im_o, 'opening')
pylab.subplot(223), plot_image(im_c, 'closing')
pylab.subplot(224), plot_image(im_oc, 'opening + closing')
pylab.show()

The following screenshot shows the output of the previous code. As can be seen, the successive application of opening and closing cleans the noisy binary fingerprint image: